Notice bibliographique
Résumé
The DB2 LUW Optimizer: Beginner to Intermediate Guide is a hands-on workshop for people who both are new or have some experienced using the DB2 SQL Optimizer (aka compiler). The workshop provided a high-level overview and basic understanding of the DB2 Optimizer while at the same time focusing on the DB2 Tools and Features for the DB2 Optimizer. The topics were presented in a workshop style, which used an interactive hands-on approach. The use of interactive exercises reinforced the topics that were presented over the course of the workshop. In this workshop the following topics were covered: • Section 1 Introduction to the Workshop Database and an SQL Primer • Section 2 Phases of the DB2 LUW Optimizer • Section 3 High Level Overview • Section 4 Explain Facility • Section 5 DB2EXFMT Tool • Section 6 Operators • Section 7 Predicates and Joins • Section 8 Basic Tuning Hints • Section 9 Catalog Statistics • Section 10 Cardinality Estimates (filter factor/selectivity) • Section 11 Statistical Views • Section 12 Optimizer Guidelines This also included interactive hands-on exercises using a database prepared specifically for this workshop. The presentation began with an introduction to the database created, which was used in the hands-on exercises. The database was created with tables of data to illustrate the topics presented. This introduction to the database also included a review of basic SQL. The next section was an introduction to the DB2 LUW Optimizer. The different phases of the Optimizer were discussed from the time the query entered the compiler to the time it was executed. The following section continued to provide a high level overview of the sections in the DB2 Explain Report and also the key components that influence the Optimizer when generating an Access Plan for any query. To understand the Access Plan generated by the DB2 Optimizer the Explain Facility was discussed in the next section. This also included a brief description of the DB2 Explain Tools available to view the Access Plan. With a better understanding of how to generate an Access Plan of a query for review the next section described the various operators used in an Access Plan. These operators were used in the generated Access Plan to show how the result set will be processed. In conjunction with the next section, where predicates and joins were discussed in more detail, exercises were used to illustrate how an SQL query would be translated into an Access Plan. The remaining sections built upon the general knowledge of Access Plans for an SQL query on how to influence changes in the Access Plan. In the Basic Tuning Hints section, there was a brief discussion on how the Database Manager and Database Configuration Parameters along with the DB2 Registry Variables can influence the DB2 Optimizer. In the next the two sections the Catalog Statistics and Cardinality Estimates were introduced. The statistics for a table describes the size of the data in the table. However, there are additional features of DB2 LUW that allow more control over the DB2 Optimizer. These two features are Statistical Views and Optimizer Guidelines, which were presented in the last two sections of the workshop.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».